Scenario-Based E-Learning: A Step Beyond Traditional E-Learning
By Randall W. Kindley

Have you noticed that there are two distinct types of e-learning?

Consider the following example: Text or narration tells you about a particular device. You're exposed to its features, told why the features are important, and shown how the components interact. Finally, a test asks you to identify the device, its components (or the concept and related ideas), and the functions they fulfill.

Now, consider a different example. A picture or video thrusts you into a realistic scenario. The situation is described, and you're given descriptions of possible outcomes. Whether the results are good or bad depends on your actions. You make decisions, as each branches into additional choices.

The first example is one of traditional e-learning, the second is an example of scenario-based e-learning. Many e-learning developers currently build the former option. But traditional page-turners are giving way to more realistic and fluid forms of e-learning. The industry is moving from traditional knowledge-related learning outcomes to an emphasis on increased internalized learning in which learners are able to assess situations and react appropriately. Knowledge components are becoming an important side-effect of a dynamic learning experience.

Reality is the ultimate learning situation. Some forms of learning attempt to get as close as possible to this ideal. Simulations, for instance, represent the complexity of a real job situation. In the 1970s, I was involved with simulating international crisis situations, where teams representing national decision makers would negotiate, review computer-generated information, and make decisions. The simulations were realistic and some of the activities could get quite heated. Everyone in our field is familiar with the Thiagi-style games and simulations that trainers frequently use in classrooms.

Granted, it's difficult to make e-learning as realistic as those in-class simulations, though many are working toward this ideal. Although creating technical simulations is fairly straightforward, such as how to fly an airplane, it's more difficult to simulate realistic social situations. After reaching a given level of technical proficiency in any job, an employee reaches a plateau where change in job performance is a function of his or her ability to interact with others in social situations. Scenario-based learning moves developers and learners off that plateau.

So, is there a middle ground between page-turners and traidtional classroom instruction? Yes. The answer lies in scenario-based e-learning. In simple terms, scenario-based learning is learning that occurs in a context, situation, or social framework. It's based on the concept of situated cognition, which is the idea that knowledge can't be known and fully understood independent of its context.

According to Michael Hebron in an interview on the Charlie Rose Show, scenario-based learning suggests that learning is a natural byproduct of "authentic activities that are common to the community of practice in which the learner is involved." In fact, Michael Hebron, a well-known golf instructor suggests that there's little the expert can do in the way of teaching the learner particular motions of the swing. Instead, learning has to be experiential and feedback based; only a handful of basic principles are involved. The same goes, he says, for any and all kinds of learning. "It’s about learning, not about golf."

Scenario-based learning is similar to the experiential model of learning. The adherents of experiential learning are fairly adamant about how people learn. Learning seldom takes place by rote. Learning occurs because we immerse ourselves in a situation in which we're forced to perform. We get feedback from our environment and adjust our behavior. We do this automatically and with such frequency in a compressed timeframe that we hardly notice we're going through a learning process. Indeed, we may not even be able to recite particular principles or describe how and why we engaged in a specific behavior. Yet, we're still able to replicate that behavior with increasing skill as we practice. If we were to ask Michael Jordan to map out the actions that describe his drive, reverse, and back-handed layup, he would probably look at us dumbfounded and say, "I just do it."

On the other hand, I'm sure Michael Jordan could diagram and spell out the use of zone and man-to-man defense, against which types of opponents each should be used, and which teams and players fit each category. Obviously, particular subjects correspond best to particular approaches. The first kind of learning is so complex with innumerable variables capable of giving feedback at several different increments, that it's all but impossible to describe. We know a good move when we see it and can identify some basic principles, but a task flowchart is out of the question.

Developing scenario-based e-learning

Ground scenario-based learning in a particular learning philosophy. Scenario-based learning best fits an open philosophy of blended and multiple learning solutions in which change and experimentation are valued and the lines between training, performance improvement, and organizational development are blurred. For scenario-based learning to be effective, the inputs to scenario and learning objective development, as well as the outputs back to the work environment in support of new behaviors, must be intimately connected with the original instructional design.

The scenario approach generally follows a performance improvement imperative. The focus is on improved outcomes rather than the acquisition of knowledge and skills. In the language of Kirkpatrick, our sights have to be set on level three and four outcomes, if not level five (ROI). We must begin with behavioral changes. Whatever the subject, the philosophy points to the successful application of ideas, which means that even highly technical training should include learning the social interactions required to achieve maximum performance.

Also, a central tenet of this philosophy is that changed performance is a function of the immediate and tangible rewards received for successful behavior. A sophisticated chain of psychological events occurs from the initial phases of learning about a subject to the internalization of habitual behavior required for successful interaction in a learning scenario. These reinforcements must be woven into the fabric of the learning experience.

High-level design considerations required before building scenario-based learning are

• an open learning philosophy encompassing blended learning, with strong connections to other organizational development activities
• a performance improvement imperative behind the training function
• rewards and reinforcements that ensure the transfer of training.

Understand how scenario-based learning differs from traditional approaches and identify when its use is appropriate. The table below compares the more traditional or knowledge-based learning to scenario-based learning.

Design learning solutions with both approaches in mind. My personal philosophy is that all training, to be successful, requires a blended, performance-based, and reinforced solution. Into the mix of typical instructional design activities, especially those around the meta-objectives and purposes of the training, I prefer to inject a categorization process to determine the subject areas that require very dense knowledge development and those that require higher levels of interaction and practice.

Let's review the basketball example. Clearly, the team's objective is to win, which means scoring more points than the other team. That's the performance objective. Each member of the team also has his or her personal performance goals. As a little league coach, I know that I can stand at a blackboard and explain defensive and offensive diagrams with players, the rules of the game, and so forth. By doing that, I have identified a set of learning subjects (rules and play patterns) that are best delivered in a traditional fashion. Without a doubt, training is a key to victory.

On the other hand, the application of these subjects and the level of proficiency required in their use can only be learned on the court. The scenario in this example is a scrimmage. During a typical scrimmage, experienced players are mixed with non-experienced players and pit against a similarly constituted practice team. The two teams play a game, and the coaches stop the action at appropriate intervals to offer feedback. Learning takes place in a highly iterative fashion--often, without the player realizing that specific bits of learning are taking place. The scrimmage provides a player with the opportunity to make several decisions, engage in complex and fast-paced behaviors, and immediately see impact. As a coach, while I have general ideas of basketball in mind and perhaps some specific learning objectives for the day, I seldom know precisely which of them I will address during the scrimmage--that depends on the flow of the practice.

Similarly, most corporate training consists of both kinds of subjects: those amenable to traditional instructional design techniques and those better approached through scenario-based learning. In point of fact, neither is all that useful without the other. Before a learner can engage in a scenario, he or she needs some basic subject knowledge. However, the strongest adherents of the scenario-based approach suggest very little subject knowledge is needed in order to take advantage of SBL. In my opinion, knowledge without application is worth very little.

Conduct discovery sessions and interactive meetings. Issues and learning objectives gathered during discovery sessions and interactive meetings must be added to those obtained through an expert-based or traditional list of higher-level learning and performance objectives. Then, we can establish appropriate techniques and where to use them.

Figure 1: Learning Chunks

Determine concept and activity boundaries and assign learning objectives. In the basketball example, players need some rudimentary knowledge of the game in order to make the practice session efficient and effective. We arrange knowledge objects in the service of actual sessions of practice. So, like a train pulling a number of boxcars, a traditional piece of learning precedes a scenario, with the scenario dictating what information is covered in the traditional piece. This is where we see strong adherents of experiential learning minimize the traditional training sections. If, as described in the scrimmage session above, you don't precisely know what will come up in the practice, you shouldn't waste time in the traditional preparation. It’s more efficient to share very basic principles and devote your resources to preparing to coach any situation that may arise.

What's important, however, is to establish the boundaries of the scenarios. These are done using behavior-based learning objectives as opposed to knowledge-based learning objectives, and are worded as performance objectives rather than skill-based behavior objectives.

For example, the matrix below illustrates the differences in the kinds of learning objectives that might be found in a traditional course versus its corresponding scenario-based learning sections. In the traditional, more knowledge intensive sections, objectives are behavior-based and tend to be specific and limited. But what we're trying to determine is whether the learner has the minimum necessary knowledge to qualify for the scenario. On the other hand, in the scenario-based objectives, we're looking for performance behaviors and indicators of internalized responses, which are usually situational recognition indicators.

Design scenarios using appropriate interactive discovery techniques. Include the appropriate stakeholders or community of practice early in the design process. Sessions should resemble focus groups in which participants work through a series of issues, from broad scenario outlines to very specific scenario details. Direct participants to address three general areas: content, style, and media.

Sessions to determine content usually ask particpants to

• share experiences about the subject event
• describe desirable outcomes
• share best practices or known instances of consistent achievement of the desired outcomes
• create indicators of successful outcomes
• create strategies expected to lead to successful outcomes
• establish descriptions of successful and unsuccessful performance behaviors related to these strategies (note that outcome measures and performance behaviors will constitute the evaluative criteria for assessing performance in the scenario).

After the content discussion, ask participants to review the look, feel, and flow of the scenario. This is much like the process used for instructional design. Develop a storyboard with a general beginning and end, using the boundaries established earlier. Act out the scenario in the session and, through iteration, establish a set of paths and data needs. Generally, a style emerges from this process. Keep records and create a flow script from the results.

With content and style established, determine media to assure the best motivation for the learner. A host of creative talent decisions and design properties such as engagement, ease of use, and navigation are attached to points along the storyboard.

Begin the alpha design and programming of the scenario. With those three elements in place, you can begin the actual programming of the scenario. A subcommittee of the designing stakeholders reviews the product in alpha and beta testing phases. Review and revise scenarios for their fit into the whole e-learning project.

While it's not the purpose of this article to explore programming techniques, you will want to use some form of animation to illustrate aspects of the scenario. Many of the standard e-learning design programs can accommodate animated sequences. You can use simple animated GIF’s, Macromedia Flash, digital video, and so forth. The important thing is to follow the storyboard and to be creative about the display of information. Your goal is to have the learner step into the situation and perceive correct responses as behaviors, which will best help him or her achieve the desired outcomes, knowing that tangible rewards will follow appropriate behavior. The approach and design techniques are very similar to commercial computer games. (See "Digital Game-Based Learning" by Marc Prensky.)

Test learner performance in the scenario by assessing behaviors. Scenarios are meant to simulate real situations. In an ideal world, an assessment team would evaluate behavior and agree on several critical performance dimensions. The key indicators should come out of the initial stakeholder activities, in which they also create strategies expected to lead to successful outcomes and establish descriptions of successful and unsuccessful performance behaviors. Outcome measures and performance behaviors will constitute the evaluative criteria for assessing performance in the scenario.

Examples of indicators of successful outcomes are whether a sale was ultimately made and for how much or if a customer was successfully served. Strategies are clusters of internally consistent behaviors directed toward the achievement of a goal. Performance behaviors are the key behaviors in those strategies. Establishing these dimensions is a group process and is usually completed in the stakeholder design session. Panel observation and scoring in addition to objective data collection is used to rate learners and create an aggregate scenario score.

A perfect assessment center isn't really possible in an e-learning situation, though. We're forced to develop alternatives to panel assessment, which is another complicating factor. A panel of human assessors can process a tremendous amount of data through simple observation, allowing an individual to make a large number of possible decisions and to take paths with scarcely a second notice by the panel. To create a custom computer program that complex would be extremely expensive. Both human and processing constraints limit the way learners can be tested in such scenarios.

One answer to this dilemma is to ask stakeholder-designers to revisit the scenario in the design session. Ask them to articulate two or three key lessons they would like the learner to internalize. Then return to the storyboards and highlight a success path for that lesson and have stakeholders select no more than two failure paths. The model is to manage no more than two or three paths, with less than three decision nodes each. Simulation-based learning, as opposed to scenario-based learning, would plan for a much larger number of paths. But this method serves well for conveying key concepts.

Figure 2: A Simple Scenario Expressed in Normal Form

Obviously, it's at the decision nodes that we assess performance. After having described the scenario and the particular decision situation, learners are given a set of choices. The stakeholder-designers have established the correct choice, which is rewarded by allowing the learner to continue on to the next scenario element and decision node. The philosophy is to reinforce correct path selection with immediate and tangible rewards.

Consider the following sales example. We can work through a few screen shots along the main success path. The first graphic displays scenario information and a decision point. In the working version, the customer would mill around the display area, which would be the behavioral cue to the sales person to initiate contact.

Figure 3: Simple Scenario Example, Decision Node #1
 

Figure 4: Simple Scenario Example, Decision Node #2
 

Figure 5: Simple Scenario Example, Decision Node #3
 

Figure 6: Simple Scenario Example, Decision Node #4

Figure 7: Simple Scenario Example, Decision Node #5
 

Review, obtain learner feedback, and revise. All learning, even the most traditional, is iterative. The key to creating a useful scenario is to see it as a learning experience for the stakeholder-designers and the learners. This means that results and comments about the learning experience are shared with the stakeholders and the designer so they can develop the scenarios. Obtain open-ended qualitative data from the learner about the experience and review the data with the stakeholder-designers.

Based on this kind of feedback, scenarios can be revised to better target the learner population. That process mirrors the original design steps. There are some cautions, however, in the revision process. First, there's an old saying in the scenario and simulation community: "It doesn’t take a cannon to blow away a tin can." Basically, revisions shouldn't needlessly complicate the scenario or the technology needed to employ it. It's crucial to weigh the risks of complication against the genuine learning needs. Before any revision, affirm the original purpose statement and the categorization of learning elements.

Also, don't let principles and main points become diluted by revisions. It's tempting to add more items and nuances in a scenario, but doing so further complicates the learning process. Save complexity for a full-scale simulation. Remember, adding an item in traditional learning complicates the learning process in a linear fashion. In scenarios, complication grows non-linearly with the addition of learning items. So, beware. A rule of thumb is to reduce rather than increase principles and main points in a revision.

Always review success and fail paths for realism. Remember that any change in a scenario item complicates all items on the path following it. Any time a decision node is altered, chances are that the decision nodes and information items following it must change. With every revision, follow and ensure the consistency of associated paths.

Finally, all e-learning solutions should employ both traditional and scenario-based e-learning. But remember, traditional e-learning elements should service the scenario-based e-learning elements, which are situated in a real context and based on the idea that knowledge can't be known and fully understood independent of its context. It's essential to place boundaries around scenarios to make the transitions between scenarios and traditional e-learning as efficient as possible.

Published: May 2002